Automated valuation model with customizable neighborhood determination

ABSTRACT

Automated valuation model with customizable neighborhood determination. A map image is displayed corresponding to a geographical area, and then user input accommodates definition of a particularly defined geographic area to provide custom identification of a neighborhood to be subject to automated valuation. Once the defined geographic area is established, the automated valuation model is applied to property data corresponding to properties within the defined geographic area. A subject property and corresponding properties within the defined geographic area are then displayed on a map image, preferably with articulation of the defined geographic area as the neighborhood of interest. The neighborhood may be defined by, among other criteria, inclusion within a user-defined shape, as well as exclusion of a user-defined shape from a displayed geographic area.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This application relates to real estate automated valuation models andmore particularly to a real estate valuation model with customizableneighborhood determination.

2. Description of the Related Art

Automated valuation models (AVM) have been developed to estimateproperty values. However, a typical AVM performs estimation based uponpredetermined inflexible geographical data sets. This may not beespecially useful for particular applications.

For example, the sales comparison approach of real estate valuationrelies heavily on the definition of neighborhood: a geographic area fromwhich relevant comparable sales to the subject can be identified. Anincorrectly-defined neighborhood would either miss relevant comparablesales or include irrelevant comparables sales (or both) and lead toinaccurate valuation.

Traditional AVM models have implemented fixed geographical standards todefine the area subject to automated valuation. AVM systems thataccommodate a more tailored approach to property value estimation areneeded.

SUMMARY OF THE INVENTION

The present invention provides an automated valuation model withcustomized neighborhood determination.

In one example, a map image is displayed corresponding to a geographicalarea, and then user input accommodates definition of a particularlydefined geographic area to provide custom identification of aneighborhood to be subject to automated valuation. Once the definedgeographic area is established, the automated valuation model is appliedto property data for properties within the defined geographic area. Asubject property and corresponding properties within the definedgeographic area are then displayed on a map image, preferably withdemarcation of the defined geographic area as the neighborhood ofinterest. The neighborhood may be defined by, among other criteria,inclusion within a user-defined shape, as well as exclusion of auser-defined shape from a displayed geographic area.

The present invention can be embodied in various forms, includingbusiness processes, computer implemented methods, computer programproducts, computer systems and networks, user interfaces, applicationprogramming interfaces, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other more detailed and specific features of the presentinvention are more fully disclosed in the following specification,reference being had to the accompanying drawings, in which:

FIGS. 1A-B are block diagrams illustrating examples of systems includinga comparable property analysis application with customized neighborhooddetermination.

FIG. 2 is a block diagram illustrating an example of a comparableproperty analysis application.

FIG. 3 is a display diagram illustrating an example of a geographicfilter designation interface.

FIG. 4 is a flow diagram illustrating an example of a process formodeling comparable properties including customized neighborhooddetermination.

FIG. 5 is a flow diagram illustrating an example of a process fordetermining a defined geographic area.

FIG. 6 is a flow diagram illustrating an example of determining adefined geographic area based upon exclusion of an identified geographicarea.

FIG. 7 is a flow diagram illustrating an example of determining adefined geographic area based upon a displayed map image.

FIG. 8A is a display diagram illustrating an example of a map image andcorresponding property grid data for a list of comparable properties ina customized neighborhood determined by inclusion within a shape on themap image.

FIG. 8B is a display diagram illustrating an example of a map image andcorresponding property grid data for a list of comparable properties ina customized neighborhood determined by exclusion from a shapecorresponding to an otherwise-defined geographic area.

FIG. 9 is a flow diagram illustrating an example of a process formodeling comparable properties including customized neighborhooddetermination based upon a hedonic regression based model.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for purposes of explanation, numerousdetails are set forth, such as flowcharts and system configurations, inorder to provide an understanding of one or more embodiments of thepresent invention. However, it is and will be apparent to one skilled inthe art that these specific details are not required in order topractice the present invention.

The present invention provides an automated valuation model withcustomized neighborhood determination. In one example, a map image isdisplayed corresponding to a geographical area, and then user inputaccommodates definition of a particularly defined geographic area toprovide custom identification of a neighborhood to be subject toautomated valuation. Once the defined geographic area is established,the automated valuation model is applied to property data for propertieswithin the defined geographic area. A subject property and correspondingproperties within the defined geographic area are then displayed on amap image, preferably with demarcation of the defined geographic area asthe neighborhood of interest. The neighborhood may be defined by, amongother criteria, inclusion within a user-defined shape, as well asexclusion of a user-defined shape from a displayed geographic area.

FIGS. 1A-B are block diagrams illustrating examples of systems includinga comparable property analysis application with customized neighborhooddetermination. Specifically, FIG. 1A illustrates several user devices102 a-c each having a comparable property analysis application 104 a-c.

The user devices 102 a-d are preferably computer devices, which may bereferred to as workstations, although they may be any conventionalcomputing device. The network over which the devices 102 a-d communicatemay also implement any conventional technology, including but notlimited to cellular, WiFi, WLAN, LAN, or combinations thereof.

In one embodiment, the comparable property analysis application 104 a-cis an application that is installed on the user device 102 a-c. Forexample, the user device 102 a-c may be configured with a web browserapplication, with the application configured to run in the context ofthe functionality of the browser application. This configuration mayalso implement a network architecture wherein the comparable propertyanalysis applications 104 a-c provide, share and rely upon thecomparable property analysis application 104 a-c functionality.

As an alternative, as illustrated in FIG. 1B, the computing devices 106a-c may respectively access a server 108, such as through conventionalweb browsing, with the server 108 providing the comparable propertyanalysis application 110 for access by the client computing devices 106a-c. As another alternative, the functionality may be divided betweenthe computing devices and server. Finally, of course, a single computingdevice may be independent configured to include the comparable propertyanalysis application.

As illustrated in FIGS. 1A-B, property data resources 112 are typicallyaccessed externally for use by the comparable property analysisapplication, since the amount of property data is rather voluminous, andsince the application is configured to allow access to any county orlocal area in a very large geographical area (e.g., for an entirecountry such as the United States). Additionally, the property dataresources 110 are shown as a singular block in the figure, but it shouldbe understood that a variety of resources, including company-internalcollected information (e.g., as collected by Fannie Mae), as well asexternal resources, whether resources where property data is typicallyfound (e.g., MLS, tax, etc.), or resources compiled by an informationservices provider (e.g., Lexis).

The comparable property analysis application 104 a-c, 110 accesses andretrieves the property data from these resources in support of themodeling of comparable properties as well as the rendering of map imagesof subject properties and corresponding comparable properties, and thedisplay of supportive data (e.g., in grid form) in association with themap images.

The comparable property analysis application 104 a-c, 110 also includescustomized neighborhood determination 118 a-c, 120. For example, a mapimage is displayed corresponding to a geographical area, and then userinput accommodates definition of a particularly defined geographic areato provide custom identification of a neighborhood to be subject toautomated valuation. Once the defined geographic area is established,the automated valuation model is applied to property data correspondingto properties within the defined geographic area. The automatedvaluation model may be of any type, including hedonic regression, priorsales, hybrid, or others.

A subject property and corresponding properties within the definedgeographic area are then displayed on a map image, preferably withdemarcation of the defined geographic area (i.e., highlightedboundaries) as the neighborhood of interest. The neighborhood may bedefined by inclusion within a user-defined shape, exclusion of auser-defined shape from a previously defined geographic area, the set ofproperties within a given distance from a subject property, propertiescorresponding to a tract or adjacent tracts, or properties currentlydisplayed on a map image (which may be manipulated as desired, prior touser indication to lock in the defined area).

FIG. 2 is a block diagram illustrating an example of a comparableproperty analysis application 200. The application 200 preferablycomprises program code that is stored on a computer readable medium(e.g., compact disk, hard disk, etc.) and that is executable by aprocessor to perform operations in support of modeling and mappingcomparable properties.

According to one aspect, the application includes program codeexecutable to perform operations of defining a particular geographicarea as the neighborhood of interest, accessing property datacorresponding to the geographical area, and applying an automatedvaluation model. A preferred AVM involves a regression based upon theproperty data, with the regression modeling the relationship betweenprice and explanatory variables.

Refinement and assessment of potential comparables may also beoptionally carried out as follows. A subject property and a plurality ofcomparable properties are identified, followed by determining a set ofvalue adjustments for each of the plurality of comparable propertiesbased upon differences in the explanatory variables between the subjectproperty and each of the plurality of comparable properties. An economicdistance between the subject property and each of the comparableproperties is determined, with the economic distance constituted as aquantified value determined from the set of value adjustments for eachrespective comparable property. Once the properties are identified andthe adjustments are determined, there may be a weighting of theplurality of comparable properties based upon the appropriateness ofeach of the plurality of comparable properties as comparables for thesubject property, the weighting being based upon one or more of theeconomic distance from the subject property, geographic distance fromthe subject property, and age of transaction.

The application 200 also includes program code for displaying a mapimage corresponding to the geographical area, and displaying indicatorson the map image indicative of the subject property and at least one ofthe plurality of comparable properties, as well as ranking the pluralityof comparable properties based upon the weighting, and displaying a textlisting of the plurality of comparable properties according to theranking.

The application 200 also includes program code for neighborhoodcustomization and corresponding valuation. The neighborhoodcustomization is preferably provided along with the display of mapimages and related data. This allows the user to interact with the mapimage to provide appropriate input to generate a shape or the like thatdefines the geographic area that in turn identifies the customizedneighborhood. Once the neighborhood is defined, automated valuation isapplied to identify the best comparable properties for a subjectproperty within the defined geographic area. Then, the map image can beupdated to display the comparable properties, typically along with thesubject property, along with indication of the defined geographicarea/neighborhood on the map image.

The application 200 provides various options for defining the geographicarea. These include definition based upon the tract of the subjectproperty (and adjacent tracts), based upon the displayed map image(i.e., the currently-displayed screen), a customizable shape thatdefines the perimeter of the defined geographic area, a customizableshape that defines an exclusion area, and distance from a subjectproperty.

The comparable property analysis application 200 is preferably providedas software, but may alternatively be provided as hardware or firmware,or any combination of software, hardware and/or firmware. Theapplication 200 is configured to provide the comparable propertymodeling and mapping functionality described herein. Although onemodular breakdown of the application 200 is offered, it should beunderstood that the same functionality may be provided using fewer,greater or differently named modules.

The example of the comparable property analysis application 200 of FIG.2 includes a property data access module 202, regression module 204, acustomized neighborhood module 205, an adjustment and weighting module206, appraisal information module 207, and UI module 208, with the UImodule 208 further including a property and appraisal selection module210, map image access module 212, indicator determining and renderingmodule 214 and property data grid/DB module 216.

The property data access module 202 includes program code for carryingout access to and management of the property data, whether from internalor external resources. The regression module 204 includes program codefor carrying out the regression upon the accessed property data,according to the regression algorithm described below, and producescorresponding results such as the determination of regressioncoefficients and other data at the country (or other) level asappropriate for a subject property. The regression module 204 mayimplement any conventional code for carrying out the regression giventhe described explanatory variables and property data.

The customized neighborhood module 205 provides interfaces and receivesinput pursuant to defining a geographic area to provide customidentification of a neighborhood subject to automated valuation.Examples of defining the neighborhood include inclusion, exclusion,distance, tract and display as described elsewhere herein.

The adjustment and weighting module 206 is configured to apply theexclusion rules, and to calculate the set of adjustment factors for theindividual comparables, the economic distance, and the weighting of thecomparables.

The appraisal information module 207 may be a stand-alone database ormay organize access to a variety of external databases of appraisalinformation. The appraisal information is typically in the form ofappraisal reports for subject properties, wherein a set of comparableproperties chosen by an appraiser is listed. The appraisal informationmay be retrieved based upon a variety of criteria, including search bysubject property, identification number, or characteristics (appraiserID, vendor, date, etc.).

The UI module 208 manages the display and receipt of information toprovide the described functionality. It includes a property andappraisal selection module 210, to manage the interfaces and input usedto identify one or more subject properties and corresponding appraisalinformation. The map image access module 212 accesses mapping functionsand manages the depiction of the map images as well as the indicators ofthe subject property and the comparable properties. The indicatordetermination and rendering module 214 is configured to manage whichindicators should be indicated on the map image depending upon thecurrent map image, the weighted ranking of the comparables andpredetermined settings or user input. The property data grid/DB 216manages the data set corresponding to a current session, including thesubject property and pool of comparable properties. It is configured asa database that allows the property data for the properties to bedisplayed in a tabular or grid format, with various sorting according tothe property characteristics, economic distance, geographical distance,time, etc.

FIG. 3 is a display diagram illustrating an example of a geographicfilter designation interface 300, with Tract 302, Map 304, Distance 306,Carve In 308 and Carve Out 310 indicated. The interface 300 allowsselection of the corresponding mode through which the geographic areawill be defined.

Under the “Tract” mode, the comparable analysis application will lookfor comparable sales in the Census Tract of the subject property, andall contiguous Census Tracts. Because the Census Bureau has tried toidentify homogenous areas in the process of defining a Census Tract,this mode is believed to provide an easy but effective method ofidentifying relevant comparable sales to be used in valuation model.

In the “Map” mode, the comparable analysis application will look forcomparable sales in the geographical area shown in the map window. Themap window can be manipulated (zoom in, zoom out, move) usingconventional commands prior to an indication to identify the current mapimage as the defined geographic area.

In the “Distance” mode, the comparable valuation model will look forcomparable sales within a distance of the subject property. The distancemay, for example, be input by the user.

In the “Carve In” mode, the comparable valuation model looks forcomparable sales within the defined geographic area.

Finally, in the Carve Out” mode, the comparable analysis applicationlooks for comparable sales, excepting as candidates the propertieswithin the defined geographic area.

FIG. 4 is a flow diagram illustrating an example of a process 400 formodeling comparable properties including customized neighborhooddetermination. The process entails determining 402 the neighborhoodfiltering criteria, such as the five modes described above.

A map image is displayed 404 and necessary input is obtained to definethe geographic area. In the Tract mode, this merely entails selection orother identification of the subject property, as the property and thecontiguous tracts define the geographic area. In the Distance mode, thesubject property and desired distance define the geographic area. In themap mode, the map image is manipulated (if desired) and then uponindication the geographic area is set as the currently-displayedgeographic area.

The Carve In and Carve Out modes entail interfacing with the user toreceive indications to define the shape that in turn defines thegeographic area. This may be a manual stringing of segments to define ashape such as a polygon that forms a perimeter of the defined geographicarea. Alternatively, a shape tool allows the user to overlay and thenresize and manipulate the shape to configure it as desired, so as tomatch it to whatever the user deems to be the appropriate neighborhood.Automated assistance may also be provided, wherein the applicationidentifies and then suggests a possible boundary of the shape, such as amajor road, body of water or the like.

Once the defined geographic area is established, the automated valuationmodel is applied 406 to corresponding property data for propertiesdesignated by the defined geographic area (whether by inclusion, as withMap, Tract, Distance or Carve In modes, or exclusion as with Carve Outmode). Although any automated valuation model may be used, an example ofa hedonic regression model is described in detail below.

Application of the model identifies a set of model-chosen comparableproperties. The rendering 408 of the map image is then updated toinclude the subject property and the comparable properties so as toillustrate their relative locations. The boundaries of the definedgeographic area may be retained in the map image rendering forappreciation that the comparables are within the desired neighborhood.Additionally, grid data concerning comparable property details may beconcurrently displayed alongside the map image.

FIG. 5 is a flow diagram illustrating an example of a process 500 fordetermining a defined geographic area, particularly for a Carve In orCarve Out mode wherein a shape is defined on the map image.

The process 500 entails displaying 502 the map image. Presumably,although not necessarily, the user has already established the subjectproperty. The subject property is displayed on the map image along withsurrounding detail. The map image may be variously manipulated usingconventional navigational commands, so that the desired level ofgranularity is displayed. Typical depictions will include roads,geographic features such as bodies of water, building names, etc. Thisallows the user to assess the general area pursuant to identifyingpotential neighborhood definitions. Once the map image is at the desiredstate, the user may initiate the shape-defining process by indicating504 a starting point on the map image. This may, for example, entail amouse click or touch screen indication of a point on the map image.

Following this, segmenting is applied until a completed shape isdefined. Automated shape generation assistance automatically identifiescandidates for a next segment based upon the current state. If thisfacility is determined 506 to be ON, then the next candidate segment isautomatically identified 512, such as through identification ofprominent geographic features. For example, the point selected as theinitial starting point may be along the border of a highway or body ofwater. The suggested segment may then be a border of such a feature.Alternatively, if no prominent feature is adjacent to the current point,then the closest road, extending from the point to the closest prominentfeature, may be identified as the next candidate segment.

The candidate segment may be highlighted on the map image for acceptanceor alteration by the user. If the suggestion is accepted (514), then itis confirmed 510 as the next segment in the shape. If not (514), thenthe process continues with either another candidate segment beingsuggested if the automated shape assistance remains ON, or manualindication of the next segment being received 508 if the automated shapeassistant is OFF.

Confirmation 510 of next segments continues until it is determined 516that the shape is completed (e.g., by user indication or by automaticdetermination that a polygonal shape has been formed, etc.). Then, thecompleted shape may be adopted 518 as articulating the definedgeographic area.

FIG. 6 is a flow diagram illustrating an example of a process 600 ofdetermining a defined geographic area based upon exclusion. Initially, abase geographic area is determined 602. This may be variouslyaccommodated. In one example, it may correspond to thecurrently-displayed map image, such as in the Map mode. Alternatively, acounty, Census Block Group or other predetermined level of granularitymay be used for the base geographic area. As another alternative, afirst, relatively larger shape may be identified, such as through thesegmenting process described in connection with FIG. 5.

Following this, a Carve Out area of exclusion, or excluded geographicarea, is identified 604 within the base geographic area. This may alsoentail various input modes as with the initial definition of the basegeographic area. The defined geographic area is thus determined 606 asbeing the base geographic area, less the Carve Out area of exclusion.

Following this, the automated valuation model is applied 608 accordingto the defined geographic area. In this fashion, the most appropriatemodel-chosen comparable properties consistent with the definedgeographic area—the customized neighborhood—are identified.

FIG. 7 is a flow diagram illustrating an example of a process 700 ofdetermining a defined geographic area based upon a displayed map image.The map image is displayed 702 and may be updated 706 through receipt704 of conventional navigational commands including zoom based commandsand movement along any axis in the plane of the image. At any given timethe user may decide to adopt the currently-displayed image as thedefined geographic area, and command is received 708 accordingly. Oncethe defined geographic area is established, the automated valuationmodel is similarly applied 710 according to the defined geographic area.

FIG. 8A is a display diagram illustrating an example of a map image 810a and corresponding property grid data 820 a for a list of comparableproperties in a customized neighborhood determined by inclusion within ashape 818 a on the map image 810 a, and FIG. 8B is a display diagram 800b illustrating an example of a map image 810 b and correspondingproperty grid data 820 b for a list of comparable properties in acustomized neighborhood determined by exclusion from a shape 818 bcorresponding to an otherwise-defined geographic area (e.g., the shownarea, or a corresponding county).

The map image 810 a-b depicts a region that can be manipulated to show alarger or smaller area, or moved to shift the center of the map image,in convention fashion. This allows the user to review the location ofthe subject property 812 and corresponding comps 814 at any desiredlevel of granularity. This map image 810 a-b may be separately viewed ona full screen, or may be illustrated alongside the property data grid820 a-b as shown.

The property grid data 820 a-b contains a listing of details about thesubject property and the comparable properties, as well as variousinformation fields. The fields include an identifier field (e.g., “S”indicates the subject property, “AS” indicates an appraiser-chosencomparable property, and a blank cell indicates a model-chosencomparable property), the address of the property (“Address”), thesquare footage (“Sq Ft”), the lot size (“Lot”), the age of the property(“Age”), the number of bathrooms (“Bath”), the prior sale amount(“Amount”), the economic distance (“ED”), geographic distance (“GD”) andtime distance (“TD”, e.g., as measured in days) factors as describedfurther below, the weight (“N. Wgt”), the ranking by weight (“Rnk”), andthe valuation as determined from the comparable sales model (“ModelVal”).

For example, FIG. 8A illustrates an example of a display screen 800 athat concurrently displays a map image 810 a and a correspondingproperty data grid 820 a. As indicated in the property grid data, thelisting identified as “S” is the subject property, and the listings withno identifier in that column are the model-chosen comparable properties.The subject property 812 and model-chosen comparable properties 814 areindicated in the map image as well.

Further assessment of the data can be variously undertaken by the user.The map image 810 a-b also allows the user to place a cursor over any ofthe illustrated properties to prompt highlighting of information forthat property and other information. Additionally, the listing ofcomparables in the property grid data 820 a-b can be updated accordingto any of the listed columns. The grid data can be variously sorted toallow the user to review how the subject property compares to the listedcomparable properties.

Still further, the map image 810 a-b can be divided into regions to helpfurther assess the location of the subject property and correspondingproperties. For example, the map image can be updated to indicateseveral Census Block Group (CBG) regions in the map image, along withtrend or other data particular to each CBG. This helps the user tofurther assess how the subject property relates to the comparableproperties, with the CBG acting as a proxy for neighborhood.

The user may variously update the map image and manipulate the propertydata grid in order to review and assess and subject property and thecorresponding comparable properties in a fashion that is both flexibleand comprehensive.

FIG. 9 is a flow diagram illustrating an example of a process 200 formodeling comparable properties, which may be performed by the comparableproperty analysis application.

As has been described, the application identifies the defined geographicarea corresponding to the customized neighborhood and accesses propertydata according to the defined geographic area (902). The definedgeographic area may be according to any of the various techniques asdescribed above and is preferably tailored at a geographical area ofinterest in which a subject property is located.

A regression 904 modeling the relationship between price and explanatoryvariables is then performed on the accessed data. Although variousalternatives may be applied, a preferred regression is that describedabove, wherein the explanatory variables are the four propertycharacteristics (GLA, lot size, age, number of bathrooms) as well as thecategorical fixed effects (location, time, foreclosure status).

A subject property within the county is identified 906 as is a pool ofcomparable properties. As described, the subject property may beinitially identified, which dictates the selection and access to theappropriate county level data. Alternatively, a user may be reviewingseveral subject properties within a county, in which case the countydata will have been accessed, and new selections of subject propertiesprompt new determinations of the pool of comparable properties for eachparticular subject property.

The pool of comparable properties may be initially defined usingexclusion rules. This limits the unwieldy number of comparables thatwould likely be present if the entire county level data were included inthe modeling of the comparables.

Although a variety of exclusion rules can be used, in one example theymay include one or more of the following: (1) limiting the comparableproperties to those within the same census tract as the subject property(or, the same census tract and any adjacent tracts); (2) including onlycomparable properties where the transaction (e.g., sale) is within 12months of the effective date of the appraisal or transaction (sale); (3)requiring GLA to be within a range including that of the subjectproperty (e.g., +/−50% of the GLA of the subject property); (4)requiring the age of the comparable properties to be within an assignedrange as determined by the age of the subject property (e.g., asdescribed previously); and/or (5) requiring the lot size for thecomparable properties to be within an assigned range as determined bythe lot size of the subject property (e.g., as described previously).

Once the pool is so-limited, a set of adjustment factors is determined908 for each remaining comparable property. The adjustment factors maybe a numerical representation of the price contribution of each of theexplanatory variables, as determined from the difference between thesubject property and the comparable property for a given explanatoryvariable. An example of the equations for determining these individualadjustments has been provided above.

Once these adjustment factors have been determined 908, the “economicdistance” between the subject property and respective individualcomparable properties is determined 910. The economic distance ispreferably constituted as a quantified value representative of theestimated price difference between the two properties as determined fromthe set of adjustment factors for each of the explanatory variables.

Following determining of the economic distance, the comparableproperties are weighted 912 in support of generating a ranking of thecomparable properties according to the model. A preferred weightingentails a function inversely proportional to the economic distance,geographic distance and age of transaction (typically sale) of thecomparable property from the subject property.

The weights may further be used to calculate an estimated price of thesubject property comprising a weighted average of the adjusted price ofall of the comparable properties.

Once the model has performed the regression, adjustments and weightingof comparables, the information is conveyed to the user in the form ofgrid and map image displays to allow convenient and comprehensive reviewand analysis of the set of comparables (914).

An example of a hedonic equation, exclusion rules, adjustments, andcorresponding weighting for display in a ranked listing are providedbelow.

(i) Hedonic Equation

Various models may be used to generate the model-chosen comparableproperties, including but not limited to one using a hedonic regressiontechnique.

One example of a hedonic equation is described below. In the hedonicequation, the dependent variable is sale price and the explanatoryvariables can include the physical characteristics, such as gross livingarea, lot size, age, number of bedrooms and or bathrooms, as well aslocation specific effects, time of sale specific effects, propertycondition effect (or a proxy thereof). This is merely an example of onepossible hedonic model. The ordinarily skilled artisan will readilyrecognize that various different variables may be used in conjunctionwith the present invention.

In this example, the dependent variable is the logged sale price. Theexplanatory variables are:

(1) Four continuous property characteristics:

(a) log of gross living area (GLA),

(b) log of Lot Size,

(c) log of Age, and

(d) Number of Bathrooms; and

(2) Three fixed effect variables:

(a) location fixed effect (e.g., by Census Block Group (CBG));

(b) Time fixed effect (e.g., measured by 3-month periods (quarters)counting back from the estimation date); and

(c) Foreclosure status fixed effect, which captures the maintenancecondition and possible REO discount.

The exemplary equation (Eq. 1) is as follows:

$\begin{matrix}{{\ln (p)} = {{\beta_{gla} \cdot {\ln ({GLA})}} + {\beta_{lot} \cdot {\ln ({LOT})}} + {\beta_{age} \cdot {\ln ({AGE})}} + {{\beta_{bath} \cdot {{BATH}++}}{\sum\limits_{i = 1}^{N_{CBG}}\; {LOC}_{i}^{CBG}}} + {\sum\limits_{j = 1}^{N_{QTR}}\; {TIME}_{j}} + {\sum\limits_{k = {\{{0,1}\}}}\; {FCL}_{k}} + ɛ}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

The above equation is offered as an example, and as noted, there may bedepartures. For example, although CBG is used as the location fixedeffect, other examples may include Census Tract or other units ofgeographical area. Additionally, months may be used in lieu of quarters,or other periods may be used regarding the time fixed effect. These andother variations may be used for the explanatory variables.

Additionally, although the county may be used for the relatively largegeographic area for which the regression analysis is performed, otherareas such as a multi-county area, state, metropolitan statistical area,or others may be used. Still further, some hedonic models may omit oradd different explanatory variables.

(ii) Exclusion Rules

Comparable selection rules are then used to narrow the pool of comps toexclude the properties which are determined to be insufficiently similarto the subject.

A comparable property should be located in a relative vicinity of thesubject and should be sold relatively recently; it should also be ofsimilar size and age and sit on a commensurate parcel of land. The “N”comparables that pass through the exclusion rules are used for furtheranalysis and value prediction.

For example, the following rules may be used to exclude comparablespursuant to narrowing the pool:

(1) Neighborhood: comps must be located in the Census Tract of thesubject and its immediate neighboring tracts;

(2) Time: comps must be sales within twelve months of the effective dateof appraisal or sale;

(3) GLA must be within a defined range, for example:

$\frac{2}{3} \leq \frac{{GLA}_{S}}{{GLA}_{C}} \leq \frac{3}{2}$

(4) Age similarity may be determined according to the following Table 1:

TABLE 1 Subject Age 0-2 3-5 6-10 11-20 21-40 41-65 65+ Acceptable 0-50-10 2-20 5-40 11-65 15-80 45+ Comp Age

(5) Lot size similarity may be determined according to the followingTable 2:

TABLE 2 Subject <2000 sqft 2000-4000 sqft 4000 sqft- >3 acres Lot size 3acres Acceptable Comp Lot 1-4000 sqft 1-8000 sqft$\frac{2}{5} \leq \frac{{LOT}_{S}}{{LOT}_{C}} \leq \frac{5}{2}$ >1 acre

These exclusion rules are provided by way of example. There may be a setof exclusion rules that add variables, that omit one or more thedescribed variables, or that use different thresholds or ranges.

(iii) Adjustment of Comps

Given the pool of comps selected by the model, the sale price of eachcomp may then be adjusted to reflect the difference between a given compand the subject in each of the characteristics used in the hedonic priceequation.

For example, individual adjustments are given by the following set ofequations (2):

A _(gla)=exp └(ln(GLA _(S))−ln(GLA _(C)))·β_(gla)┘;

A _(lot)=exp [(ln(LOT_(S))−ln(LOT_(C)))·β_(lot)];

A _(age)=exp └(ln(AGE_(S))−ln(AGE_(C)))·β_(age)┘;

A _(bath)=exp └(BATH_(S)−BATH_(C))·β_(age)┘;

A _(loc)=exp [LOC _(S) −LOC _(C)];

A _(time)=exp [TIME_(S)−TIME_(C)]; and

A _(fcl)=exp [FCL _(S)−FCL_(C)],  (Eq. 2)

where coefficients βgla, βlot, βage, βbath, LOC, TIME, FCL are obtainedfrom the hedonic price equation described above. Hence, the adjustedprice of the comparable sales is summarized as:

$\begin{matrix}{p_{C}^{adj} = {{p_{C} \cdot {\prod\limits_{i \in {\{{{gla},{lot},{age},{bath},{loc},{time},{fcl}}\}}}\; A_{i}}} = {p_{C} \cdot A_{TOTAL}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

(iv) Weighting of Comps and Value Prediction

Because of unknown neighborhood boundaries and potentially missing data,the pool of comparables will likely include more than are necessary forthe best value prediction in most markets. The adjustments describedabove can be quite large given the differences between the subjectproperty and comparable properties. Accordingly, rank ordering andweighting are also useful for the purpose of value prediction.

The economic distance D_(eco) between the subject property and a givencomp may be described as a function of the differences between them asmeasured in dollar value for a variety of characteristics, according tothe adjustment factors described above.

Specifically, the economic distance may be defined as a Euclidean normof individual percent adjustments for all characteristics used in thehedonic equation:

$\begin{matrix}{D_{SC}^{eco} = \sqrt{\sum\limits_{i \in {\{{{gla},{lot},{age},{bath},{loc},{time},{fcl}}\}}}\; \left( {A_{i} - 1} \right)^{2}}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$

The comps are then weighted. Properties more similar to the subject interms of physical characteristics, location, and time of sale arepresumed better comparables and thus are preferably accorded more weightin the prediction of the subject property value. Accordingly, the weightof a comp may be defined as a function inversely proportional to theeconomic distance, geographic distance and the age of sale.

For example, comp weight may be defined as:

$\begin{matrix}{w_{C} = \frac{1}{D_{SC}^{eco} \cdot D_{SC}^{geo} \cdot {dT}_{SC}}} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$

where D_(geo) is a measure of a geographic distance between the comp andthe subject, defined as a piece-wise function:

$\begin{matrix}{D_{SC}^{geo} = \left\{ \begin{matrix}0.1 & {if} & {d_{SC} < {0.1\mspace{14mu} {mi}}} \\d_{SC} & {if} & {{{0.1\mspace{14mu} {mi}} \leq d_{SC} \leq {1.0\mspace{14mu} {mi}}},} \\{1.0 + \sqrt{d_{SC} - 1.0}} & {if} & {d_{SC} > {1.0\mspace{14mu} {mi}}}\end{matrix} \right.} & \left( {{Eq}.\mspace{14mu} 6} \right)\end{matrix}$

and dT is a down-weighting age of comp sale factor

$\begin{matrix}{{dT}_{SC} = \left\{ \begin{matrix}1.00 & {if} & {\left( {0,90} \right\rbrack \mspace{14mu} {days}} \\1.25 & {if} & {\left( {90,180} \right\rbrack \mspace{14mu} {days}} \\2.00 & {if} & {\left( {180,270} \right\rbrack \mspace{14mu} {days}} \\2.50 & {if} & {\left( {270,365} \right\rbrack \mspace{14mu} {days}}\end{matrix} \right.} & \left( {{Eq}.\mspace{14mu} 7} \right)\end{matrix}$

Comps with higher weight receive higher rank and consequently contributemore value to the final prediction, since the predicted value of thesubject property based on comparable sales model is given by theweighted average of the adjusted price of all comps:

$\begin{matrix}{{\hat{p}}_{S} = \frac{\sum\limits_{C = 1}^{N_{COMPS}}\; {w_{C} \cdot p_{C}^{adj}}}{\sum\limits_{C = 1}^{N_{COMPS}}\; w_{C}}} & \left( {{Eq}.\mspace{14mu} 8} \right)\end{matrix}$

As can be seen from the above, the separate weighting following thedetermination of the adjustment factors allows added flexibility inprescribing what constitutes a good comparable property. Thus, forexample, policy factors such as those for age of sale data or locationmay be separately instituted in the weighting process. Although oneexample is illustrated it should be understood that the artisan will befree to design the weighting and other factors as necessary.

(v) Listing and Mapping of Comparable Properties

The comparable properties may then be listed according to the weighting,or a ranking from the highest weighted comparable property to thelowest. This listing may be variously limited to accommodate listingthem within a display area. For example, a default setting might be 20comparable properties. The overall list of comparable propertiesincludes, of course, the model-chosen comparable properties. The overalllist can also include appraiser-chosen comparables, such as when anappraisal report is being evaluated by comparing the report comparablesto those indicated as best by the model.

Mapping and analytical tools that implement the comparable model areprovided. Mapping features allow the subject property and comparableproperties to be concurrently displayed. Additionally, a table or gridof data for the subject properties is concurrently displayable so thatthe list of comparables can be manipulated, with the indicators on themap image updating accordingly.

For example, mapping features include the capability to display theboundaries of census units, school attendance zones, neighborhoods, aswell as statistical information such as median home values, average homeage, etc.

The grid/table view allows the user to sort the list of comparables onrank, value, size, age, or any other dimension. Additionally, the rowsin the table are connected to the full database entry as well as salehistory for the respective property. Combined with the map view and theneighborhood statistics, this allows for a convenient yet comprehensiveinteractive analysis of comparable sales.

Thus embodiments of the present invention produce and provide methodsand apparatus for automated valuation with customized neighborhooddetermination. Although the present invention has been described inconsiderable detail with reference to certain embodiments thereof, theinvention may be variously embodied without departing from the spirit orscope of the invention. Therefore, the following claims should not belimited to the description of the embodiments contained herein in anyway.

1. A method property valuation, the method comprising: prompting display of a map image corresponding to a geographical area; receiving an identification of a defined geographic area within the geographical area, the defined geographic area being defined through received user input associated with display of the map image; accessing property data corresponding to properties within the defined geographic area; applying an automated valuation to the property data after receiving the identification of the defined geographic area; and displaying information for at least one property in the defined geographic area based upon results of applying the automated valuation.
 2. The method of claim 1, wherein the defined geographic area is a polygon, wherein the received user input identifies a plurality of segments constituting the polygon.
 3. The method of claim 1, wherein the defined geographic area is defined according a plurality of segments, wherein the received user input identifies the plurality of segments in free form fashion.
 4. The method of claim 1, wherein the defined geographic area is defined according to a plurality of segments, and at least one of the plurality of segments is automatically suggested for selection based upon correlation of the at least one segment to a geographical feature in the map image.
 5. The method of claim 1, wherein the defined geographic area is defined according a distance from a subject property, such that the automated valuation is applied only to the property data for properties within the distance from the subject property.
 6. The method of claim 1, wherein the defined geographic area is defined as being coincident with a currently displayed map image.
 7. The method of claim 1, further comprising: receiving an identification of a subject property; ranking comparable properties in the defined geographic area based upon the results of the automated valuation; and displaying indicators on the map image indicative of the subject property and at least one of the comparable properties.
 8. The method of claim 1, wherein applying the automated valuation comprises: performing a regression based upon the property data, the regression modeling the relationship between price and explanatory variables; identifying candidate comparable properties within the defined geographic area; determining a set of value adjustments for the candidate comparable properties based upon differences in the explanatory variables between a subject property and the candidate comparable properties; and determining an economic distance between the subject property and respective ones of the candidate comparable properties, the economic distance constituted as a quantified value determined from the set of value adjustments for each respective comparable property, wherein the ranking of comparable properties is determined based upon a weighting of the candidate comparable properties based upon the economic distance from the subject property.
 9. A non-transitory computer readable medium storing program code for property valuation, the program code being executable to perform operations comprising: prompting display of a map image corresponding to a geographical area; receiving an identification of a defined geographic area within the geographical area, the defined geographic area being defined through received user input associated with display of the map image; accessing property data corresponding to properties within the defined geographic area; applying an automated valuation to the property data after receiving the identification of the defined geographic area; and displaying information for at least one property in the defined geographic area based upon results of applying the automated valuation.
 10. The computer readable medium of claim 9, wherein the defined geographic area is a polygon, wherein the received user input identifies a plurality of segments constituting the polygon.
 11. The computer readable medium of claim 9, wherein the defined geographic area is defined according a plurality of segments, wherein the received user input identifies the plurality of segments in free form fashion.
 12. The computer readable medium of claim 9, wherein the defined geographic area is defined according to a plurality of segments, and at least one of the plurality of segments is automatically suggested for selection based upon correlation of the at least one segment to a geographical feature in the map image.
 13. The computer readable medium of claim 9, wherein the defined geographic area is defined according a distance from a subject property, such that the automated valuation is applied only to the property data for properties within the distance from the subject property.
 14. The computer readable medium of claim 9, wherein the defined geographic area is defined as being coincident with a currently displayed map image.
 15. The computer readable medium of claim 9, wherein the operations further comprise: receiving an identification of a subject property; ranking comparable properties in the defined geographic area based upon the results of the automated valuation; and displaying indicators on the map image indicative of the subject property and at least one of the comparable properties.
 16. The computer readable medium of claim 9, wherein applying the automated valuation comprises: performing a regression based upon the property data, the regression modeling the relationship between price and explanatory variables; identifying candidate comparable properties within the defined geographic area; determining a set of value adjustments for the candidate comparable properties based upon differences in the explanatory variables between a subject property and the candidate comparable properties; and determining an economic distance between the subject property and respective ones of the candidate comparable properties, the economic distance constituted as a quantified value determined from the set of value adjustments for each respective comparable property, wherein the ranking of comparable properties is determined based upon a weighting of the candidate comparable properties based upon the economic distance from the subject property.
 17. A system for property valuation, the system comprising: means for prompting display of a map image corresponding to a geographical area; means for receiving an identification of a defined geographic area within the geographical area, the defined geographic area being defined through received user input associated with display of the map image; means for accessing property data corresponding to properties within the defined geographic area; means for applying an automated valuation to the property data after receiving the identification of the defined geographic area; and means for displaying information for at least one property in the defined geographic area based upon results of applying the automated valuation.
 18. The system of claim 17, wherein the defined geographic area is a polygon, wherein the received user input identifies a plurality of segments constituting the polygon.
 19. The system of claim 17, wherein the defined geographic area is defined according a plurality of segments, wherein the received user input identifies the plurality of segments in free form fashion.
 20. The system of claim 17, wherein the defined geographic area is defined according to a plurality of segments, and at least one of the plurality of segments is automatically suggested for selection based upon correlation of the at least one segment to a geographical feature in the map image.
 21. The system of claim 17, wherein the defined geographic area is defined according a distance from a subject property, such that the automated valuation is applied only to the property data for properties within the distance from the subject property.
 22. The system of claim 17, wherein the defined geographic area is defined as being coincident with a currently displayed map image.
 23. The system of claim 17, further comprising: means for receiving an identification of a subject property; ranking comparable properties in the defined geographic area based upon the results of the automated valuation; and means for displaying indicators on the map image indicative of the subject property and at least one of the comparable properties.
 24. The system of claim 17, wherein applying the automated valuation comprises: performing a regression based upon the property data, the regression modeling the relationship between price and explanatory variables; identifying candidate comparable properties within the defined geographic area; determining a set of value adjustments for the candidate comparable properties based upon differences in the explanatory variables between a subject property and the candidate comparable properties; and determining an economic distance between the subject property and respective ones of the candidate comparable properties, the economic distance constituted as a quantified value determined from the set of value adjustments for each respective comparable property, wherein the ranking of comparable properties is determined based upon a weighting of the candidate comparable properties based upon the economic distance from the subject property. 